To make the inference stages tractable, some works e.g., [1]}, [2]}, [3]}, [4]}, [5]} used numeric approaches such as particle filtering to approximate the probability of the states in continuous state spaces. Another set of approaches e.g., [6]}, [7]}, [8]}, [9]}, [10]} discretize the state space to deal with a limited number of hidden variables during the inference. Discretizing the state spaces makes it possible to use some efficient inference algorithms
such as the forward algorithm to compute the most likely posterior probability of the hidden states.
